Noise filtering through unsupervised learning can learn a robust fingerprint representation prior to location estimation.
The work in [1]} proposes a denoising-contractive autoencoder to learn the RSS-based fingerprint that captures the variation induced by the human body while filtering out the noise contributed by the environmental factor.
On the other hand, [2]} mainly focuses on getting rid of any noise present in the fingerprint vector through a deep autoencoder so that it can achieve a finer location estimation with cleaner representation.
The work in [3]} defines a walking step model to automatically calibrate the fingerprint vector and at the same time filter out the possible noise through a deep neural network.
Even though noise filtering is essential in learning a better fingerprint representation, it is still unable to learn the fingerprint representation that is invariant to the holding position of a smartphone.
Note that some noise, for example, those induced by multipath effects, is useful information to enhance the localization.
Rather than applying any unsupervised learning methods to perform noise filtering, our work directly selects those beacons with the least RSS variation in connection to the dynamic holding position, while retaining those noise information for localization training.
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